OpenRaiser/Intern-Atlas huggingface dataset:
Generative AI-Enabled Adaptive Learning Platform: How I Can Help You Pass Your Driving Test?
Venue: arXiv cs.HC 2025
Year: 2025

This study aims to develop an adaptive learning platform that leverages generative AI to automate assessment creation and feedback delivery. The platform provides self-correcting tests and personalised feedback that adapts to each learners progress and history, ensuring a tailored learning experience. The study involves the development and evaluation of a web-based application for revision for the UK Driving Theory Test. The platform generates dynamic, non-repetitive question sets and offers adaptive feedback based on user performance over time. The effectiveness of AI-generated assessments and feedback is evaluated through expert review and model analysis. The results show the successful generation of relevant and accurate questions, alongside positive and helpful feedback. The personalised test generation closely aligns with expert-created assessments, demonstrating the reliability of the system. These findings suggest that generative AI can enhance learning outcomes by adapting to individual student needs and offering tailored support. This research introduces an AI-powered assessment and feedback system that goes beyond traditional solutions by incorporating automation and adaptive learning. The non-memoryless feedback mechanism ensures that student history and performance inform future assessments, making the learning process more effective and individualised. This contrasts with conventional systems that provide static, one-time feedback without considering past progress.
Modality-Aware Infrared and Visible Image Fusion with Target-Aware Supervision
Venue: arXiv cs.CV 2025
Year: 2025

Infrared and visible image fusion (IVIF) is a fundamental task in multi-modal perception that aims to integrate complementary structural and textural cues from different spectral domains. In this paper, we propose FusionNet, a novel end-to-end fusion framework that explicitly models inter-modality interaction and enhances task-critical regions. FusionNet introduces a modality-aware attention mechanism that dynamically adjusts the contribution of infrared and visible features based on their discriminative capacity. To achieve fine-grained, interpretable fusion, we further incorporate a pixel-wise alpha blending module, which learns spatially-varying fusion weights in an adaptive and content-aware manner. Moreover, we formulate a target-aware loss that leverages weak ROI supervision to preserve semantic consistency in regions containing important objects (e.g., pedestrians, vehicles). Experiments on the public M3FD dataset demonstrate that FusionNet generates fused images with enhanced semantic preservation, high perceptual quality, and clear interpretability. Our framework provides a general and extensible solution for semantic-aware multi-modal image fusion, with benefits for downstream tasks such as object detection and scene understanding.
Multiple Instance Learning Framework with Masked Hard Instance Mining for Gigapixel Histopathology Image Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Digitizing pathological images into gigapixel Whole Slide Images (WSIs) has opened new avenues for Computational Pathology (CPath). As positive tissue comprises only a small fraction of gigapixel WSIs, existing Multiple Instance Learning (MIL) methods typically focus on identifying salient instances via attention mechanisms. However, this leads to a bias towards easy-to-classify instances while neglecting challenging ones. Recent studies have shown that hard examples are crucial for accurately modeling discriminative boundaries. Applying such an idea at the instance level, we elaborate a novel MIL framework with masked hard instance mining (MHIM-MIL), which utilizes a Siamese structure with a consistency constraint to explore the hard instances. Using a class-aware instance probability, MHIM-MIL employs a momentum teacher to mask salient instances and implicitly mine hard instances for training the student model. To obtain diverse, non-redundant hard instances, we adopt large-scale random masking while utilizing a global recycle network to mitigate the risk of losing key features. Furthermore, the student updates the teacher using an exponential moving average, which identifies new hard instances for subsequent training iterations and stabilizes optimization. Experimental results on cancer diagnosis, subtyping, survival analysis tasks, and 12 benchmarks demonstrate that MHIM-MIL outperforms the latest methods in both performance and efficiency. The code is available at: https://github.com/DearCaat/MHIM-MIL.
On the Distinctive Co-occurrence Characteristics of Antonymy
Venue: arXiv cs.CL 2025
Year: 2025

Antonymy has long received particular attention in lexical semantics. Previous studies have shown that antonym pairs frequently co-occur in text, across genres and parts of speech, more often than would be expected by chance. However, whether this co-occurrence pattern is distinctive of antonymy remains unclear, due to a lack of comparison with other semantic relations. This work fills the gap by comparing antonymy with three other relations across parts of speech using robust co-occurrence metrics. We find that antonymy is distinctive in three respects: antonym pairs co-occur with high strength, in a preferred linear order, and within short spans. All results are available online.
D$^2$HScore: Reasoning-Aware Hallucination Detection via Semantic Breadth and Depth Analysis in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Although large Language Models (LLMs) have achieved remarkable success, their practical application is often hindered by the generation of non-factual content, which is called "hallucination". Ensuring the reliability of LLMs' outputs is a critical challenge, particularly in high-stakes domains such as finance, security, and healthcare. In this work, we revisit hallucination detection from the perspective of model architecture and generation dynamics. Leveraging the multi-layer structure and autoregressive decoding process of LLMs, we decompose hallucination signals into two complementary dimensions: the semantic breadth of token representations within each layer, and the semantic depth of core concepts as they evolve across layers. Based on this insight, we propose \textbf{D$^2$HScore (Dispersion and Drift-based Hallucination Score)}, a training-free and label-free framework that jointly measures: (1) \textbf{Intra-Layer Dispersion}, which quantifies the semantic diversity of token representations within each layer; and (2) \textbf{Inter-Layer Drift}, which tracks the progressive transformation of key token representations across layers. To ensure drift reflects the evolution of meaningful semantics rather than noisy or redundant tokens, we guide token selection using attention signals. By capturing both the horizontal and vertical dynamics of representation during inference, D$^2$HScore provides an interpretable and lightweight proxy for hallucination detection. Extensive experiments across five open-source LLMs and five widely used benchmarks demonstrate that D$^2$HScore consistently outperforms existing training-free baselines.
Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification
Venue: arXiv cs.CV 2025
Year: 2025

Unsupervised visible-infrared person re-identification (USVI-ReID) aims to learn modality-invariant image features from unlabeled cross-modal person datasets by reducing the modality gap while minimizing reliance on costly manual annotations. Existing methods typically address USVI-ReID using cluster-based contrastive learning, which represents a person by a single cluster center. However, they primarily focus on the commonality of images within each cluster while neglecting the finer-grained differences among them. To address the limitation, we propose a Hierarchical Identity Learning (HIL) framework. Since each cluster may contain several smaller sub-clusters that reflect fine-grained variations among images, we generate multiple memories for each existing coarse-grained cluster via a secondary clustering. Additionally, we propose Multi-Center Contrastive Learning (MCCL) to refine representations for enhancing intra-modal clustering and minimizing cross-modal discrepancies. To further improve cross-modal matching quality, we design a Bidirectional Reverse Selection Transmission (BRST) mechanism, which establishes reliable cross-modal correspondences by performing bidirectional matching of pseudo-labels. Extensive experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate that the proposed method outperforms existing approaches. The source code is available at: https://github.com/haonanshi0125/HIL.
Analyzing Information-Seeking Behaviors in a Hakka AI Chatbot: A Cognitive-Pragmatic Study
Venue: arXiv cs.CL 2025
Year: 2025

With many endangered languages at risk of disappearing, efforts to preserve them now rely more than ever on using technology alongside culturally informed teaching strategies. This study examines user behaviors in TALKA, a generative AI-powered chatbot designed for Hakka language engagement, by employing a dual-layered analytical framework grounded in Bloom's Taxonomy of cognitive processes and dialogue act categorization. We analyzed 7,077 user utterances, each carefully annotated according to six cognitive levels and eleven dialogue act types. These included a variety of functions, such as asking for information, requesting translations, making cultural inquiries, and using language creatively. Pragmatic classifications further highlight how different types of dialogue acts--such as feedback, control commands, and social greetings--align with specific cognitive intentions. The results suggest that generative AI chatbots can support language learning in meaningful ways--especially when they are designed with an understanding of how users think and communicate. They may also help learners express themselves more confidently and connect with their cultural identity. The TALKA case provides empirical insights into how AI-mediated dialogue facilitates cognitive development in low-resource language learners, as well as pragmatic negotiation and socio-cultural affiliation. By focusing on AI-assisted language learning, this study offers new insights into how technology can support language preservation and educational practice.
HalluDetect: Detecting, Mitigating, and Benchmarking Hallucinations in Conversational Systems in the Legal Domain
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) are widely used in industry but remain prone to hallucinations, limiting their reliability in critical applications. This work addresses hallucination reduction in consumer grievance chatbots built using LLaMA 3.1 8B Instruct, a compact model frequently used in industry. We develop HalluDetect, an LLM-based hallucination detection system that achieves an F1 score of 68.92% outperforming baseline detectors by 22.47%. Benchmarking five hallucination mitigation architectures, we find that out of them, AgentBot minimizes hallucinations to 0.4159 per turn while maintaining the highest token accuracy (96.13%), making it the most effective mitigation strategy. Our findings provide a scalable framework for hallucination mitigation, demonstrating that optimized inference strategies can significantly improve factual accuracy.
Inducing Uncertainty on Open-Weight Models for Test-Time Privacy in Image Recognition
Venue: arXiv cs.LG 2025
Year: 2025

A key concern for AI safety remains understudied in the machine learning (ML) literature: how can we ensure users of ML models do not leverage predictions on incorrect personal data to harm others? This is particularly pertinent given the rise of open-weight models, where simply masking model outputs does not suffice to prevent adversaries from recovering harmful predictions. To address this threat, which we call *test-time privacy*, we induce maximal uncertainty on protected instances while preserving accuracy on all other instances. Our proposed algorithm uses a Pareto optimal objective that explicitly balances test-time privacy against utility. We also provide a certifiable approximation algorithm which achieves $(\varepsilon, δ)$ guarantees without convexity assumptions. We then prove a tight bound that characterizes the privacy-utility tradeoff that our algorithms incur. Empirically, our method obtains at least $>3\times$ stronger uncertainty than pretraining with marginal drops in accuracy on various image recognition benchmarks. Altogether, this framework provides a tool to guarantee additional protection to end users.
Active Sequential Hypothesis Testing with Non-Homogeneous Costs
Venue: arXiv cs.IT 2025
Year: 2025

We study the Non-Homogeneous Sequential Hypothesis Testing (NHSHT), where a single active Decision-Maker (DM) selects actions with heterogeneous positive costs to identify the true hypothesis under an average error constraint \(δ\), while minimizing expected total cost paid. Under standard arguments, we show that the objective decomposes into the product of the mean number of samples and the mean per-action cost induced by the policy. This leads to a key design principle: one should optimize the ratio of expectations (expected information gain per expected cost) rather than the expectation of per-step information-per-cost ("bit-per-buck"), which can be suboptimal. We adapt the Chernoff scheme to NHSHT, preserving its classical \(\log 1/δ\) scaling. In simulations, the adapted scheme reduces mean cost by up to 50\% relative to the classic Chernoff policy and by up to 90\% relative to the naive bit-per-buck heuristic.
IS-Diff: Improving Diffusion-Based Inpainting with Better Initial Seed
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models have shown promising results in free-form inpainting. Recent studies based on refined diffusion samplers or novel architectural designs led to realistic results and high data consistency. However, random initialization seed (noise) adopted in vanilla diffusion process may introduce mismatched semantic information in masked regions, leading to biased inpainting results, e.g., low consistency and low coherence with the other unmasked area. To address this issue, we propose the Initial Seed refined Diffusion Model (IS-Diff), a completely training-free approach incorporating distributional harmonious seeds to produce harmonious results. Specifically, IS-Diff employs initial seeds sampled from unmasked areas to imitate the masked data distribution, thereby setting a promising direction for the diffusion procedure. Moreover, a dynamic selective refinement mechanism is proposed to detect severe unharmonious inpaintings in intermediate latent and adjust the strength of our initialization prior dynamically. We validate our method on both standard and large-mask inpainting tasks using the CelebA-HQ, ImageNet, and Places2 datasets, demonstrating its effectiveness across all metrics compared to state-of-the-art inpainting methods.
Colour Perception in Immersive Virtual Reality: Emotional and Physiological Responses to Fifteen Munsell Hues
Venue: arXiv cs.HC 2025
Year: 2025

Colour is a fundamental determinant of affective experience in immersive virtual reality (VR), yet the emotional and physiological impact of individual hues remains poorly characterised. This study investigated how fifteen calibrated Munsell hues influence subjective and autonomic responses when presented in immersive VR. Thirty-six adults (18-45 years) viewed each hue in a within-subject design while pupil diameter and skin conductance were recorded continuously, and self-reported emotions were assessed using the Self-Assessment Manikin across pleasure, arousal, and dominance. Repeated-measures ANOVAs revealed robust hue effects on all three self-report dimensions and on pupil dilation, with medium to large effect sizes. Reds and red-purple hues elicited the highest arousal and dominance, whereas blue-green hues were rated most pleasurable. Pupil dilation closely tracked arousal ratings, while skin conductance showed no reliable hue differentiation, likely due to the brief (30 s) exposures. Individual differences in cognitive style and personality modulated overall reactivity but did not alter the relative ranking of hues. Taken together, these findings provide the first systematic hue-by-hue mapping of affective and physiological responses in immersive VR. They demonstrate that calibrated colour shapes both experience and ocular physiology, while also offering practical guidance for educational, clinical, and interface design in virtual environments.
Room acoustics affect communicative success in hybrid meeting spaces: a pilot study
Venue: arXiv cs.CL 2025
Year: 2025

Since the COVID-19 pandemic in 2020, universities and companies have increasingly integrated hybrid features into their meeting spaces, or even created dedicated rooms for this purpose. While the importance of a fast and stable internet connection is often prioritized, the acoustic design of seminar rooms is frequently overlooked. Poor acoustics, particularly excessive reverberation, can lead to issues such as misunderstandings, reduced speech intelligibility or cognitive and vocal fatigue. This pilot study investigates whether room acoustic interventions in a seminar room at Graz University of Technology support better communication in hybrid meetings. For this purpose, we recorded two groups of persons twice, once before and once after improving the acoustics of the room. Our findings -- despite not reaching statistical significance due to the small sample size - indicate clearly that our spatial interventions improve communicative success in hybrid meetings. To make the paper accessible also for readers from the speech communication community, we explain room acoustics background, relevant for the interpretation of our results.
Permutation decoding of first-order Generalized Reed-Muller codes
Venue: arXiv cs.IT 2025
Year: 2025

In [4] we describe a variation of the classical permutation decoding algorithm that can be applied to any binary affine-invariant code; in particular, it can be applied to first-order Reed-Muller codes successfully. In this paper we study how to implement it for the family of first-order Generalized Reed-Muller codes. Then, we give examples which show that we improve the number of errors we can correct in comparison with the known results for this family of codes. Finally, we deal, from a probabilistic point of view, with the problem of determining when the algorithm only needs to use a smaller PD-like set.
Augmented Reality-Enhanced Robot Teleoperation for Collecting User Demonstrations
Venue: arXiv cs.RO 2025
Year: 2025

Traditional industrial robot programming is often complex and time-consuming, typically requiring weeks or even months of effort from expert programmers. Although Programming by Demonstration (PbD) offers a more accessible alternative, intuitive interfaces for robot control and demonstration collection remain challenging. To address this, we propose an Augmented Reality (AR)-enhanced robot teleoperation system that integrates AR-based control with spatial point cloud rendering, enabling intuitive, contact-free demonstrations. This approach allows operators to control robots remotely without entering the workspace or using conventional tools like the teach pendant. The proposed system is generally applicable and has been demonstrated on ABB robot platforms, specifically validated with the IRB 1200 industrial robot and the GoFa 5 collaborative robot. A user study evaluates the impact of real-time environmental perception, specifically with and without point cloud rendering, on task completion accuracy, efficiency, and user confidence. Results indicate that enhanced perception significantly improves task performance by 28% and enhances user experience, as reflected by a 12% increase in the System Usability Scale (SUS) score. This work contributes to the advancement of intuitive robot teleoperation, AR interface design, environmental perception, and teleoperation safety mechanisms in industrial settings for demonstration collection. The collected demonstrations may serve as valuable training data for machine learning applications.
Anomaly Detection in Industrial Control Systems Based on Cross-Domain Representation Learning
Venue: arXiv cs.CR 2025
Year: 2025

Industrial control systems (ICSs) are widely used in industry, and their security and stability are very important. Once the ICS is attacked, it may cause serious damage. Therefore, it is very important to detect anomalies in ICSs. ICS can monitor and manage physical devices remotely using communication networks. The existing anomaly detection approaches mainly focus on analyzing the security of network traffic or sensor data. However, the behaviors of different domains (e.g., network traffic and sensor physical status) of ICSs are correlated, so it is difficult to comprehensively identify anomalies by analyzing only a single domain. In this paper, an anomaly detection approach based on cross-domain representation learning in ICSs is proposed, which can learn the joint features of multi-domain behaviors and detect anomalies within different domains. After constructing a cross-domain graph that can represent the behaviors of multiple domains in ICSs, our approach can learn the joint features of them by leveraging graph neural networks. Since anomalies behave differently in different domains, we leverage a multi-task learning approach to identify anomalies in different domains separately and perform joint training. The experimental results show that the performance of our approach is better than existing approaches for identifying anomalies in ICSs.
Visualization and Analysis of the Loss Landscape in Graph Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Graph Neural Networks (GNNs) are powerful models for graph-structured data, with broad applications. However, the interplay between GNN parameter optimization, expressivity, and generalization remains poorly understood. We address this by introducing an efficient learnable dimensionality reduction method for visualizing GNN loss landscapes, and by analyzing the effects of over-smoothing, jumping knowledge, quantization, sparsification, and preconditioner on GNN optimization. Our learnable projection method surpasses the state-of-the-art PCA-based approach, enabling accurate reconstruction of high-dimensional parameters with lower memory usage. We further show that architecture, sparsification, and optimizer's preconditioning significantly impact the GNN optimization landscape and their training process and final prediction performance. These insights contribute to developing more efficient designs of GNN architectures and training strategies.
Pseudo-D: Informing Multi-View Uncertainty Estimation with Calibrated Neural Training Dynamics
Venue: arXiv cs.CV 2025
Year: 2025

Computer-aided diagnosis systems must make critical decisions from medical images that are often noisy, ambiguous, or conflicting, yet today's models are trained on overly simplistic labels that ignore diagnostic uncertainty. One-hot labels erase inter-rater variability and force models to make overconfident predictions, especially when faced with incomplete or artifact-laden inputs. We address this gap by introducing a novel framework that brings uncertainty back into the label space. Our method leverages neural network training dynamics (NNTD) to assess the inherent difficulty of each training sample. By aggregating and calibrating model predictions during training, we generate uncertainty-aware pseudo-labels that reflect the ambiguity encountered during learning. This label augmentation approach is architecture-agnostic and can be applied to any supervised learning pipeline to enhance uncertainty estimation and robustness. We validate our approach on a challenging echocardiography classification benchmark, demonstrating superior performance over specialized baselines in calibration, selective classification, and multi-view fusion.